£120.00

Springer Feature Selection for Data and Pattern Recognition 584

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Description

Explore the latest advancements in computational intelligence with this research book from Springer. Feature Selection for Data and Pattern Recognition (Studies in Computational Intelligence, 584) serves as a comprehensive resource for understanding current progress and research trends in the field. Feature selection remains a vital area of investigation because of its significant impact on various data processing tasks. This volume provides a collection of high-quality texts that address the importance of this subject. Readers will find detailed information organized into four distinct parts, covering essential topics such as the estimation of characteristic feature importance, relevance, dependencies, and weighting. This book is designed for researchers and professionals looking to stay updated on new developments in pattern recognition. By focusing on the most active avenues of investigation, it offers a deep look into the methodologies used to improve data analysis and machine learning processes.

Key Features

Provides high-quality research texts focused on current progress and new developments in feature selection.

Covers essential research trends in data and pattern recognition for advanced computational intelligence.

Organized into four topical parts for structured learning and easy navigation of complex subjects.

Includes detailed studies on the estimation of importance for characteristic features.

Explores technical aspects of feature relevance, dependencies, and weighting methods.

Addresses active avenues of investigation used to solve complex data and pattern recognition problems.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
15 January 2015
Listed Since
21 October 2014

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